Search Results (527)

Various electrospinning techniques can be used to produce nanofiber mats with randomly oriented or aligned nanofibers made of different materials and material mixtures. Such nanofibers have a high specific surface area, making them sensitive as sensors for health monitoring. The entire nanofiber mats are very thin and lightweight and, therefore, can be easily integrated into wearables such as textile fabrics or even patches. Nanofibrous sensors can be used not only to analyze sweat but also to detect physical parameters such as ECG or heartbeat, movements, or environmental parameters such as temperature, humidity, etc., making them an interesting alternative to other wearables for continuous health monitoring. This paper provides an overview of various nanofibrous sensors made of different materials that are used in health monitoring. Both the advantages of electrospun nanofiber mats and their potential problems, such as inhomogeneities between different nanofiber mats or even within one electrospun specimen, are discussed. Full article

Freezing of gait (FOG) is a disabling yet poorly understood paroxysmal gait disorder affecting the vast majority of patients with Parkinson’s disease (PD) as they reach advanced stages of the disorder. Falling is one of the most disabling consequences of a FOG episode; it often results in injury and a future fear of falling, leading to diminished social engagement, a reduction in general fitness, loss of independence, and degradation of overall quality of life. Currently, there is no robust or reliable treatment against FOG in PD. In the absence of reliable and effective treatment for Parkinson’s disease, alleviating the consequences of FOG represents an unmet clinical need, with the first step being reliable FOG prediction. Current methods for FOG prediction and prevention cannot provide real-time readouts and are not sensitive enough to detect changes in walking patterns or balance. To fill this gap, we developed an sEMG system consisting of a soft, wearable garment (pair of shorts and two calf sleeves) embedded with screen-printed electrodes and stretchable traces capable of picking up and recording the electromyography activities from lower limb muscles. Here, we report on the testing of these garments in healthy individuals and in patients with PD FOG. The preliminary testing produced an initial time-to-onset commencement that persisted > 3 s across all patients, resulting in a nearly 3-fold drop in sEMG activity. We believe that these initial studies serve as a solid foundation for further development of smart digital textiles with integrated bio and chemical sensors that will provide AI-enabled, medically oriented data. Full article

The integration of textile-based sensing and actuation elements has become increasingly important across various fields, driven by the growing demand for smart textiles in healthcare, sports, and wearable electronics. This paper presents the development of a small, smart dielectric elastomer (DE)-based sensing array designed for user control input in applications such as human–machine interaction, virtual object manipulation, and robotics. DE-based sensors are ideal for textile integration due to their flexibility, lightweight nature, and ability to seamlessly conform to surfaces without compromising comfort. By embedding these sensors into textiles, continuous user interaction can be achieved, providing a more intuitive and unobtrusive user experience. The design of this DE array draws inspiration from a flexible and wearable version of a touchpad, which can be incorporated into clothing or accessories. Integrated advanced machine learning algorithms enhance the sensing system by improving resolution and enabling pattern recognition, reaching a prediction performance of at least 80. Additionally, the array’s electrodes are fabricated using a novel sputtering technique for low resistance as well as high geometric flexibility and size reducibility. A new crimping method is also introduced to ensure a reliable connection between the sensing array and the custom electronics. The advantages of the presented design, data evaluation, and manufacturing process comprise a reduced structure size, the flexible adaptability of the system to the respective application, reliable pattern recognition, reduced sensor and line resistance, the adaptability of mechanical force sensitivity, and the integration of electronics. This research highlights the potential for innovative, highly integrated textile-based sensors in various practical applications. Full article

This paper presents a pH sensor with a Mach–Zehnder Interferometer (MZI) that operates in solutions of 4.0, 7.0, and 10.0. The sensor device consists of two tapered sections with dimensions of 1 mm/1 mm/1 mm for down-taper, waist-length, and up-taper, respectively, with a separation of 10 mm. The diameter of the waist is 40 $\mathsf{\mu }$m. This work includes the experimental evaluation of an MZI fiber optic pH sensor at 1559 nm, where 1559 nm represents a specific wavelength chosen for its optimal sensitivity in evaluating the sensor pH detection performance. It is not the central wavelength of the optical fiber, but one of the minimal values selected to enhance the interaction between the evanescent field and the sample, ensuring the reliable detection of pH variations. These sensor dimensions and the functionalized solution of multi-walled carbon nanotubes (MWCNTs) increase the detection of pH in dyes used in the textile industry. Alizarin is a strong anionic red dye that is part of the anthraquinone dye group. The experimental results demonstrated effective detection of pH levels in water contamination involving dye. This development could resolve the problem with Alizarin. The simple fabrication, low cost, and stability of the optical response make this sensor relevant for pH measurements in water contamination. Full article

A therapeutic glove, which enables medical non-professionals to perform physiotherapeutic gripping and holding movements on patients, would significantly improve the healthcare situation in physiotherapy. The glove aims to detect the orthogonal pressure load and provide feedback to the user. The use of textile materials for the glove assures comfort and a good fit for the user. This, in turn, implies a textile realization of the sensor system in order to manufacture both the glove and the sensor system in as few process steps as possible, using only one textile manufacturing technique. The flat knitting technology is an obvious choice here. The aim of the study is to develop a textile capacitive pressure sensor that can be integrated into the fingertips of a glove using flat knitting technology and to evaluate its sensor properties with regard to transmission behavior, hysteresis and drift. It was shown that the proposed method of a flat knitting sensor fabrication is suitable for producing both the sensors and the glove in one single process step. In addition, the implementation of an entire glove with integrated pressure sensors, including the necessary electrical connection of the sensor electrodes via knitted conductive paths in three fingers, was successfully demonstrated. Full article

Women sports wearer’s comfort and health are greatly impacted by the breast movements and resultant sports bra compression to prevent excessive movement. However, as sports bras are only made in universal sizes, they do not offer the right kind of support that is required for a certain activity. To prevent this issue, textile-based strain sensors may be utilized to track compression throughout various activities to create activity-specific designed sports bras. Textile-based strain sensors are prepared in this study using various conductive yarns, including steel, Ag-coated polyamide, and polypropylene/steel-blended threads. Various embroidery designs, including straight, zigzag, and square-wave embroidery patterns, etc., were created on knitted fabric and characterized for strain sensing efficiencies. The experiments concluded that strain sensors prepared from polypropylene/steel thread using a 2-thread square-wave design were best performed in terms of linear conductivity, sensitivity of mechanical impact, and wide working range. This best-performed sample was also tested by integrating it into the sportswear for proposed compression measurements in different body movements. Full article

Induction-based breathing sensors in automobiles enable unobtrusive respiratory rate monitoring as an indicator of a driver’s alertness and health. This paper introduces a quantitative method based on signal quality to guide the integration of textile inductive electrodes in automotive applications. A case study with a simplified setup illustrated the ability of the method to successfully provide basic design rules about where and how to integrate the electrodes on seat belts and seat backs to gather good quality respiratory signals in an automobile. The best signals came from the subject’s waist, then from the chest, then from the upper back, and finally from the lower back. Furthermore, folding the electrodes before their integration on a seat back improves the signal quality for both the upper and lower back. This analysis provided guidelines with three design rules to increase the chance of acquiring good quality signals: (1) use a multi-electrode acquisition approach, (2) place the electrodes in locations that maximize breathing-induced body displacement, and (3) use a mechanical amplifying method such as folding the electrodes in locations with little potential for breathing-induced displacement. Full article

Currently, wearable sensors can measure vital sign frequencies, such as respiration rate, but they fall short of providing quantitative data, such as respiratory tidal volume. Meanwhile, the airflow at the mouth carries both the frequency and quantitative respiratory signals. In this study, we propose a method to calibrate a wearable piezoelectric thread sensor placed on the chest using mouth airflow for accurate quantitative respiration monitoring. Prior to human trials, we introduced an artificial ventilator as a test subject. To validate the proposed concept, we embedded a miniaturized tube airflow sensor at the ventilator’s outlet, which simulates human respiration, and attached a wearable piezoelectric thread to the piston, which moves periodically to mimic human chest movement. The integrated output readings from the wearable sensor aligned with the airflow rate measurements, demonstrating its ability to accurately monitor not only respiration rate but also quantitative metrics such as respiratory volume. Finally, tidal volume measurement was demonstrated using the wearable piezoelectric thread. Full article

Taking advantage of its high-temperature resistance and elongation properties, conductive-coated polyetheretherketone (PEEK) filament yarn can be used as a textile-based electroconductive functional element, in particular as a strain sensor. This study describes the development of electrical conductivity on an inert PEEK filament surface by the deposition of metallic nickel (Ni) layers via an electroless galvanic plating process. To enhance the adhesion properties of the nickel layer, both PEEK multifilament and monofilament yarn surfaces were metalized by plasma torch pretreatment, followed by nickel plating. Electrical characterizations indicate the potential of nickel-coated PEEK for structural monitoring in textile-reinforced composites. In addition, surface energy measurements before and after plasma torch pretreatment, surface morphology, nickel layer thickness, chemical structure changes, and mechanical properties were analyzed and compared with untreated PEEK. The thickness of the Ni layer was measured and showed an average thickness of 1.25 µm for the multifilament yarn and 3.36 µm for the monofilament yarn. FTIR analysis confirmed the presence of new functional groups on the PEEK surface after plasma torch pretreatment, indicating a successful modification of the surface chemistry. Mechanical testing showed an increase in tensile strength after plasma torch pretreatment but a decrease after nickel plating. In conclusion, this study successfully developed conductive PEEK yarns through plasma torch pretreatment and nickel plating. Full article

The development of the field of textronics covers many directions, but the neediest are safety, medicine, and environmental protection. The solutions developed can combine the needs of many people from different social groups and ages. This leads to sustainable socio-economic, scientific and integrated approaches to sustainable development. The authors, seeing the growing need to monitor air pollution in order to increase safety, decided to develop textronic chemical sensors based on carbon-based inks and metal thread embroidery, sensitive to harmful gases and vapors based on textiles. This was to limit the production of subsequent sensors made in plastic housings containing difficult-to-recycle materials and replace them with sensors incorporated into everyday materials such as clothing, which will inform us about emerging threats not only in the place where a large plastic sensor is placed, but in every place at home, at work and outside where we will be. The authors assume that the sensors can be incorporated into clothing, e.g. work clothes, and can also be fastened from one piece of clothing to another. This increases their economic aspect and usability on a larger scale. Three materials of different composition were tested: cotton, polyester and viscose. These materials were selected based on their properties, namely the easier determination of their ability to achieve full circularity of the final product.Functional and mechanical tests of resistance to factors occurring during everyday use were carried out for the use of systems in clothing materials and to produce roller blinds and curtains. To examine the durability of the systems, electrical conductivity was checked before and after the tests. The results showed changes in resistance values after individual tests and during contact with harmful gases. Particularly noticeable are the differences between samples with embroidery and samples with inkjet paste applied. It was shown that the selected materials are suitable for the intended application, and selected modifications together with conductive materials show proper functioning in detecting harmful gases. This project demonstrates the possibility of creating chemical sensors based on printing techniques using carbon printing pastes and embroidery with a metal thread with silver on a textile substrate. Possible applications considering health and environmental aspects are presented. Full article

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to reduce risks of work accidents and occupational illnesses and enhance the environmental sustainability of the processes. In this experimental study, we developed, implemented, and tested a novel algorithm that detects fabric defects by utilizing enhanced deep convolutional neural networks (DCNNs). The proposed method integrates advanced DCNN architectures to automatically classify and detect 13 different types of fabric defects, such as double-ends, holes, broken ends, etc., ensuring high accuracy and efficiency in the inspection process. The dataset is created through augmentation techniques and a model is fine-tuned on a large dataset of annotated images using transfer learning approaches. The experiment was performed using an anthropomorphic robot that was programmed to move above the fabric. The camera attached to the robot detected defects in the fabric and triggered an alarm. A photoelectric sensor was installed on the conveyor belt and linked to the robot to notify it about an impending fabric. The CNN model architecture was enhanced to increase performance. Experimental findings show that the presented system can detect fabric defects with a 97.49% mean Average Precision (mAP). Full article

Background: Motion Tape (MT) is a low-profile, disposable, self-adhesive wearable sensor that measures skin strain. Preliminary studies have validated MT for measuring lower back movement. However, further analysis is needed to determine if MT can be used to measure lower back muscle engagement. The purpose of this study was to measure differences in MT strain between conditions in which the lower back muscles were relaxed versus maximally activated. Methods: Ten participants without low back pain were tested. A matrix of six MTs was placed on the lower back, and strain data were captured under a series of conditions. The first condition was a baseline trial, in which participants lay prone and the muscles of the lower back were relaxed. The subsequent trials were maximum voluntary isometric contractions (MVICs), in which participants did not move, but resisted the examiner force in extension or rotational directions to maximally engage their lower back muscles. The mean MT strain was calculated for each condition. A repeated measures ANOVA was conducted to analyze the effects of conditions (baseline, extension, right rotation, and left rotation) and MT position (1–6) on the MT strain. Post hoc analyses were conducted for significant effects from the overall analysis. Results: The results of the ANOVA revealed a significant main effect of condition (p < 0.001) and a significant interaction effect of sensor and condition (p = 0.01). There were significant differences in MT strain between the baseline condition and the extension and rotation MVIC conditions, respectively, for sensors 4, 5, and 6 (p = 0.01–0.04). The largest differences in MT strain were observed between baseline and rotation conditions for sensors 4, 5, and 6. Conclusions: MT can capture maximal lower back muscle engagement while the trunk remains in a stationary position. Lower sensors are better able to capture muscle engagement than upper sensors. Furthermore, MT captured muscle engagement during rotation conditions better than during extension. Full article

In this work, a flexible textile-based capacitive respiratory sensor, based on a capacitive sensor structure, that does not require direct skin contact is designed, optimised, and evaluated using both computational modelling and empirical measurements. In the computational study, the geometry of the sensor was examined. This analysis involved observing the capacitance and frequency variations using a cylindrical model that mimicked the human body. Four designs were selected which were then manufactured by screen printing multiple functional layers on top of a polyester/cotton fabric. The printed sensors were characterised to detect the performance against phantoms and impacts from artefacts, normally present whilst wearing the device. A sensor that has an electrode ratio of 1:3:1 (sensor, reflector, and ground) was shown to be the most sensitive design, as it exhibits the highest sensitivity of 6.2% frequency change when exposed to phantoms. To ensure the replicability of the sensors, several batches of identical sensors were developed and tested using the same physical parameters, which resulted in the same percentage frequency change. The sensor was further tested on volunteers, showing that the sensor measures respiration with 98.68% accuracy compared to manual breath counting. Full article

Due to their advantageous characteristics, shape memory alloys (SMAs) are prominent representatives in smart materials. They can be used in application fields such as soft robotics, biomimetics, and medicine. Within this work, a fiber–elastomer composite with integrated SMA wire is developed and investigated. Bending and torsion occur when the SMA is activated because of the anisotropic structure of the textile. The metrological challenge in characterizing actuators that perform both bending and torsion lies in the large active deformation of the composite and the associated difficulties in fully imaging and analyzing this with optical measurement methods. In this work, a multi-sensor camera system with up to four pairs of cameras connected in parallel is used. The structure to be characterized is recorded from all sides to evaluate the movement in three-dimensional space. The energy input and the time required for an even deflection of the actuator are investigated experimentally. Here, the activation parameters for the practical energy input required for long life with good deflection, i.e., good efficiency, were analyzed. Different parameters and times are considered to minimize the energy input and, thus, to prevent possible overheating and damage to the wire. Thermography is used to evaluate the heating of the SMA wire at different actuation times over a defined time. Full article

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up. Full article